Abstract
Onboard hyperspectral image classification is desirable for many remotely sensed image analysis applications. This talk presents a reconfigurable accelerator capable of onboard classification of hyperspectral images based on the Support Vector Machine approach. The architecture can support high problem dimensionality and efficient use of available hardware resources. Implementation results show that the proposed accelerator can achieve significant improvement in speed and in energy efficiency over conventional computer implementations.

Currently Professor of Computer Engineering at Imperial College, he founded and leads the Computer Systems Section and the Custom Computing Group in Department of Computing, and was Visiting Professor at Stanford University and Queen’s University Belfast. He is a member of the Program Committee of many international conferences such as FCCM, FPL and FPT. He has been an author or editor for 6 books and 4 special journal issues.

Dr. Luk had 15 papers that received awards from various conferences such as ASAP, FPL, FPT, SAMOS, SPL and ERSA, and he also won a Research Excellence Award from Imperial College in 2006. He is a Fellow of the Royal Academy of Engineering and the BCS, and was founding Editor-in-Chief for ACM Transactions on Reconfigurable Technology and Systems.